Create an array of tokenized documents.

textData = ["the quick brown fox jumped over the lazy dog""the fast brown fox jumped over the lazy dog""the lazy dog sat there and did nothing""the other animals sat there watching"]; documents = tokenizedDocument(textData)

documents = 4x1 tokenizedDocument: 9 tokens: the quick brown fox jumped over the lazy dog 9 tokens: the fast brown fox jumped over the lazy dog 8 tokens: the lazy dog sat there and did nothing 6 tokens: the other animals sat there watching

Calculate the similarities between them using thecosineSimilarityfunction. The output is a sparse matrix.

similarities = cosineSimilarity(documents);

Visualize the similarities between the documents in a heat map.

figure heatmap(similarities); xlabel("Document") ylabel("Document") title("Cosine Similarities")

Scores close to one indicate strong similarity. Scores close to zero indicate weak similarity.

Create an array of input documents.

str = ["the quick brown fox jumped over the lazy dog""the fast fox jumped over the lazy dog""the dog sat there and did nothing""the other animals sat there watching"]; documents = tokenizedDocument(str)

documents = 4x1 tokenizedDocument: 9 tokens: the quick brown fox jumped over the lazy dog 8 tokens: the fast fox jumped over the lazy dog 7 tokens: the dog sat there and did nothing 6 tokens: the other animals sat there watching

Create an array of query documents.

str = ["a brown fox leaped over the lazy dog""another fox leaped over the dog"]; queries = tokenizedDocument(str)

queries = 2x1 tokenizedDocument: 8 tokens: a brown fox leaped over the lazy dog 6 tokens: another fox leaped over the dog

Calculate the similarities between input and query documents using thecosineSimilarityfunction. The output is a sparse matrix.

similarities = cosineSimilarity(documents,queries);

Visualize the similarities of the documents in a heat map.

figure heatmap(similarities); xlabel("Query Document") ylabel("Input Document") title("Cosine Similarities")

Scores close to one indicate strong similarity. Scores close to zero indicate weak similarity.

Create a bag-of-words model from the text data insonnets.csv.

文件名="sonnets.csv"; tbl = readtable(filename,'TextType','string'); textData = tbl.Sonnet; documents = tokenizedDocument(textData); bag = bagOfWords(documents)

bag = bagOfWords with properties: Counts: [154x3527 double] Vocabulary: ["From" "fairest" "creatures" "we" ... ] NumWords: 3527 NumDocuments: 154

Calculate similarities between the sonnets using thecosineSimilarityfunction. The output is a sparse matrix.

similarities = cosineSimilarity(bag);

Visualize the similarities of the first five documents in a heat map.

figure heatmap(similarities(1:5,1:5)); xlabel("Document") ylabel("Document") title("Cosine Similarities")

Scores close to one indicate strong similarity. Scores close to zero indicate weak similarity.

For bag-of-words input, thecosineSimilarityfunction calculates the cosine similarity using the tf-idf matrix derived from the model. To compute the cosine similarities on the word count vectors directly, input the word counts to thecosineSimilarityfunction as a matrix.

Create a bag-of-words model from the text data insonnets.csv.

文件名="sonnets.csv"; tbl = readtable(filename,'TextType','string'); textData = tbl.Sonnet; documents = tokenizedDocument(textData); bag = bagOfWords(documents)

bag = bagOfWords with properties: Counts: [154x3527 double] Vocabulary: ["From" "fairest" "creatures" "we" ... ] NumWords: 3527 NumDocuments: 154

Get the matrix of word counts from the model.

M = bag.Counts;

Calculate the cosine document similarities of the word count matrix using thecosineSimilarityfunction. The output is a sparse matrix.

similarities = cosineSimilarity(M);

Visualize the similarities of the first five documents in a heat map.

figure heatmap(similarities(1:5,1:5)); xlabel("Document") ylabel("Document") title("Cosine Similarities")

Scores close to one indicate strong similarity. Scores close to zero indicate weak similarity.